Studying the evolutions, differences, and water security impacts of water demands under shared socioeconomic pathways: A SEMs-bootstrap-ANN approach applied to Sichuan Province

Mo, Li; Lou, Sijing; Wang, Yongqiang; Liu, Zixuan; Ren, Pingan

doi:10.1016/j.jenvman.2023.119455

Cited by 5 publications

(1 citation statement)

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“…However, bootstrap programming adds a significant number of codes into the network, limiting the performance and processing speed of the software management system. On the other hand, the combination of artificial neural network and bootstrap algorithms improves the estimation of prediction error distributions, making it easier to analyse any faults or anomalies within the wastewater treatment systems (Mo et al 2024).…”

Section: Assessment Of Water Qualitymentioning

confidence: 99%

Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

Ganthavee,

Trzcinski

2024

Environ Chem Lett

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The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in the context of rapid industrialization and urbanization. Moreover, most pollutants such as antibiotics escape conventional wastewater treatments and are thus discharged in ecosystems, requiring advanced techniques for wastewater treatment. Here we review the use of artificial intelligence and machine learning to optimize pharmaceutical wastewater treatment systems, with focus on water quality, disinfection, renewable energy, biological treatment, blockchain technology, machine learning algorithms, big data, cyber-physical systems, and automated smart grid power distribution networks. Artificial intelligence allows for monitoring contaminants, facilitating data analysis, diagnosing water quality, easing autonomous decision-making, and predicting process parameters. We discuss advances in technical reliability, energy resources and wastewater management, cyber-resilience, security functionalities, and robust multidimensional performance of automated platform and distributed consortium, and stabilization of abnormal fluctuations in water quality parameters.

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Section: Assessment Of Water Qualitymentioning

confidence: 99%